02-Acoustic Analysis
Kelly Faller
2024-10-28
Clear Environment
Good practice to keep the environment clean and start fresh.
NOTE: Not included in knit
Load Packages
NOTE: Not included in knit
Load Data
NOTE: Use data prep scripts first before loading data! Will need to run the Arbimon Data Prep Script to get the Recordings.csv file. NOTE: Not included in knit
Run for all
Arbimon
BirdNet
Kaleidoscope
Data Wrangling - Yeehaw!
NOTE: Not included in knit
Arbimon
Kaleidoscope
Site Map
Stations are interactive. Can click the points and it will give you station name. There are also several basemap options. Toggle in between them for added fun.
pal=colorFactor(palette = "viridis", domain = NULL)
leaflet(recording_metadata) %>%
addProviderTiles("OpenStreetMap", group = "OpenStreetMap") %>%
addProviderTiles("CartoDB.Positron", group = "CartoDB.Positron") %>%
addProviderTiles("Esri.WorldImagery", group = "Esri.WorldImagery") %>%
addProviderTiles("Esri.WorldTopoMap", group = "Esri.WorldTopoMap") %>%
addProviderTiles("NASAGIBS.ViirsEarthAtNight2012", group = "NASAGIBS.ViirsEarthAtNight2012") %>%
addLayersControl(baseGroups = c("Esri.WorldImagery","OpenStreetMap", "CartoDB.Positron", "Esri.WorldTopoMap", "NASAGIBS.ViirsEarthAtNight2012"),
position = "topleft")%>%
addCircleMarkers(~longitude, ~latitude,radius=5,
fillColor = ~pal(name),
stroke = TRUE,
color="white",
weight=0.75,
fillOpacity = 0.75,
popup= ~name) %>%
addLegend("bottomleft", pal = pal, values = ~name, opacity = .98,
title = "ARU Location") %>%
fitBounds(
lng1 = min(recording_metadata$longitude),
lat1 = min(recording_metadata$latitude),
lng2 = max(recording_metadata$longitude),
lat2 = max(recording_metadata$latitude)
)Recording Schedule Graphics
Visualization of recording periods per year. This is based off of the recording data extracted from Arbimon, but applies to all the analyzed data. It is the backbone information to put all the indices and species IDs in context.
# 2023 Recording Schedule
ggplot(recording_metadata %>% filter (Year == "2023", name != "TEST Bait Site (Throwaway)"), aes(x = Julian_Day, y = name)) +
geom_segment(aes(xend = Julian_Day, yend = name), color = "gray") +
geom_point(aes(color = as.factor(name)), size = 4) +
scale_color_viridis_d() +
scale_x_continuous(breaks = seq(0, 365, by = 5)) +
labs(x = "Julian Day", y = "Recorder (Site ID)",
title = "Julian Days Each Recorder Was Active in 2023") +
theme_minimal(base_size = 15) +
theme(panel.grid.major.y = element_blank())+
theme(legend.position = "none")
# 2024 Recording Schedule
ggplot(recording_metadata %>% filter (Year == "2024"), aes(x = Julian_Day, y = name)) +
geom_segment(aes(xend = Julian_Day, yend = name), color = "gray") +
geom_point(aes(color = as.factor(name)), size = 4) +
scale_color_viridis_d() +
scale_x_continuous(breaks = seq(0, 365, by = 5)) +
labs(x = "Julian Day", y = "Recorder (Site ID)",
title = "Julian Days Each Recorder Was Active in 2024") +
coord_cartesian(xlim = c(115, 255)) +
theme_minimal(base_size = 15) +
theme(panel.grid.major.y = element_blank())+
theme(legend.position = "none")
Graphics!!!
Arbimon
Presence/Absence
Come back here and add some information :)
# Load the image
sparrow_image <- png::readPNG("C:/Users/kfaller/Documents/GitHub/Acoustics/Images/SALS.png") # Load your image
sparrow_grob <- rasterGrob(sparrow_image, interpolate = TRUE)
# SALS
# 2024
ggplot(species_IDs %>% filter(scientific_name == "Ammospiza caudacuta", Year == "2024"),
aes(x = Julian_Day, y = name, fill = factor(validated))) +
geom_tile(color = "white") + # Change tile border color to white for a cleaner look
scale_fill_manual(values = c("0" = "gray", "1" = "darkgreen"), name = "Presence") +
scale_x_continuous(breaks = seq(0, 365, by = 5)) + # Use a color palette
labs(x = "Julian Day", y = "Site ID", title = "2024 Saltmarsh Sparrow Presence/Absence") +
theme_bw(base_size = 15)+
annotation_custom(sparrow_grob, xmin=160, xmax=175, ymin=0, ymax=3)
# 2023 THIS DATA SEEMS OFF. I HAD 28 SALS LAST YEAR
ggplot(species_IDs %>% filter(scientific_name == "Ammospiza caudacuta", Year == "2023"),
aes(x = Julian_Day, y = name, fill = factor(validated))) +
geom_tile(color = "white") + # Change tile border color to white for a cleaner look
scale_fill_manual(values = c("0" = "gray", "1" = "darkgreen"), name = "Presence") +
scale_x_continuous(breaks = seq(0, 365, by = 5)) + # Use a color palette
labs(x = "Julian Day", y = "Site ID", title = "2023 Saltmarsh Sparrow Presence/Absence") +
theme_bw(base_size = 15)+
annotation_custom(sparrow_grob, xmin=160, xmax=175, ymin=0, ymax=3)
#NESP
# 2023
ggplot(species_IDs %>% filter(scientific_name == "Ammodramus nelsoni", Year == "2023"),
aes(x = Julian_Day, y = name, fill = factor(validated))) +
geom_tile(color = "white") + # Change tile border color to white for a cleaner look
scale_fill_manual(values = c("0" = "gray", "1" = "darkgreen"), name = "Presence") +
scale_x_continuous(breaks = seq(0, 365, by = 5)) + # Use a color palette
labs(x = "Julian Day", y = "Site ID", title = "2023 Nelson's Sparrow Presence/Absence") +
theme_bw(base_size = 15)+
annotation_custom(sparrow_grob, xmin=155, xmax=170, ymin=0, ymax=3) 
Kaleidoscope Acoustic Index Graphics
We are using Wildlife Acoustics’ Kaleidoscope software to take our raw audio files and output numerical values for different aspects of the soundscape for each file. Those values were then averaged to reduce the noise of the data. The indices we are including are:
- BI: Bioacoustic Index
- ADI: Average Diversity Index
- AEI: Average Evenness Index
- ACI: Acoustic Complexity Index
- NDSI: Normalized Difference Soundscape Index aka Anthropogenic Disturbance
The ACI (Pieretti et al. 2011) measures the variability within biotic sounds while ignoring anthropogenic noise. BI (Boelman et al. 2007) measures the number of frequency bands used by birds and is a proxy for avian abundance. NDSI (Kasten et al. 2012), which will be of considerable importance to our urban-to-rural gradient objectives, estimates the level of anthropogenic disturbance (i.e., noise pollution) in the soundscape. This model creates a ratio of human-generated to biological sound. Finally, ADI (Villanueva-Rivera et al. 2011), is derived from Shannon Diversity and calculates the diversity of sound. After our deployments, a large dataset will be produced, tracking these indices across our sites over time.
Averages for Indices
Accounting for two microphones (= 2 values for each time period) & creating daily average
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Daily average acoustic complexity across sites. Higher numbers represent a more complex soundscape.
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## (`geom_point()`).
Daily average acoustic evenness across sites. Higher numbers represent a more even soundscape. A healthy ecosystem should have low evenness. High evenness scores could be reflecting high rates of anthropogenic disturbance or low biodiversity. The y axis is on a natural log scale.
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Daily average acoustic diversity across sites. Higher numbers represent a more diverse soundscape. A healthy ecosystem should have high diversity. High diversity scores could be reflecting a more biodiverse community. The y axis is on a natural log scale with a 2.3 being the highest diversity possible. This index is the closest to Shannon Diversity. Includes all recorded sounds (including any anthropogenic disturbance) giving a value for the entire soundscape, rather than just the biological diversity.
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Daily average acoustic biodiversity across sites. Higher numbers represent a more diverse soundscape. This differs from the diversity index because it excludes anthropogenic disturbance making this a better index to understand the biophony of a site (or biological soundscape).
## Warning: Removed 2 rows containing missing values or values outside the scale range
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## (`geom_point()`).
Daily average anthropogenic disturbance across sites. Lower numbers represent a more anthropogenic disturbance in the soundscape. -1 represents 100% anthropogenic sounds and 1 would be 100% biological sounds.
Boxplots for Indices
Cowplot for Average Indices
One graphic showing most important acoustic indices in one overview.
day_avg_aci<- ggplot(data= day_avg_index, aes(x= DAY, y= avg_ACI, color= STATION, shape= Site_Type))+
geom_line(size=1)+
geom_point(size= 3)+
theme_bw(base_size=18)+
ggtitle("Daily Average Indices")+
labs(x= " ", y= "ACI - Complexity")+
scale_x_datetime(date_breaks = "1 day")+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
guides(color = guide_legend(title = "Station"), shape = guide_legend(title = "Site Type"))
day_avg_bi<- ggplot(data= day_avg_index, aes(x= DAY, y= avg_BI, color= STATION, shape= Site_Type))+
geom_line(size=1)+
geom_point(size= 3)+
theme_bw(base_size=18)+
labs(x= " ", y= "BI - Bioacoustics")+
scale_x_datetime(date_breaks = "1 day")+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
guides(color = guide_legend(title = "Station"), shape = guide_legend(title = "Site Type"))
day_avg_ndsi<- ggplot(data= day_avg_index, aes(x= DAY, y= avg_NDSI, color= STATION, shape= Site_Type))+
geom_line(size=1)+
geom_point(size= 3)+
theme_bw(base_size=18)+
labs(x= " ", y= "NDSI - Anthropogenic Disturbance")+
scale_x_datetime(date_breaks = "1 day")+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
guides(color = guide_legend(title = "Station"), shape = guide_legend(title = "Site Type"))+ scale_y_reverse()
plot_grid(day_avg_aci,
day_avg_bi,
day_avg_ndsi,
nrow= 3, align = "v", axis="l",
labels="AUTO")## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 2 rows containing missing values or values outside the scale range
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## (`geom_point()`).
LeeAnn Graphics
Boxplot
my_comparisons <- list(c("GAR_01", "RF_02"),
c("GAR_01", "RF_01"),
c("GAR_02", "RF_02"),
c("GAR_04", "RF_02"),
c("GAR_02", "RF_01"),
c("GAR_01", "GAR_04"),
c("RF_01", "RF_02"),
c("GAR_04", "RF_01"),
c("GAR_04", "GAR_02"),
c("GAR_01", "GAR_02")
)
box_avg_aci<- ggplot(day_avg_index, aes(x=STATION, y=avg_ACI, color= STATION))+
geom_boxplot()+
theme_bw(base_size=15)+
labs(x= " ", y= "ACI - Complexity")+
guides(color=FALSE)+
stat_compare_means(label="p.format", method="t.test", comparisons = my_comparisons,
symnum.args = list(cutpoints = c(0, 0.05, 1), symbols = c("**", "-")))+
stat_compare_means(method = "anova", label.y = +Inf, vjust=1,hjust=-0.4)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
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## generated.box_avg_bi<- ggplot(day_avg_index, aes(x=STATION,y= avg_BI, color= STATION))+
geom_boxplot()+
theme_bw(base_size=15)+
labs(x= " ", y= "BI - Bioacoustics")+
guides(color=FALSE)+
stat_compare_means(label="p.format", method="t.test", comparisons = my_comparisons,
symnum.args = list(cutpoints = c(0, 0.05, 1), symbols = c("**", "-")))+
stat_compare_means(method = "anova", label.y = +Inf, vjust=1,hjust=-0.5)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
box_avg_ndsi<- ggplot(day_avg_index, aes(x=STATION,y= avg_NDSI, color= STATION))+
geom_boxplot()+
theme_bw(base_size=15)+
labs(x= " ", y= "NDSI - Anthropogenic Disturbance")+
guides(color=FALSE)+
stat_compare_means(label="p.format", method="t.test", comparisons = my_comparisons,
symnum.args = list(cutpoints = c(0, 0.05, 1), symbols = c("**", "-")))+
stat_compare_means(method = "anova", label.y = +Inf, vjust=1,hjust=-0.4)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
plot_grid(box_avg_aci,
box_avg_bi,
box_avg_ndsi,
nrow= 1, align = "v", axis="l",
labels="AUTO")## Warning: Removed 2 rows containing non-finite outside the scale range
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## (`stat_compare_means()`).
Relying on LeeAnn’s CSV. Need to add in the code to create the time of day column in the R code.
my_comparisons1 <- list(c("morning", "mid-day"),
c("morning", "evening"),
c("morning", "night"),
c("mid-day", "evening"),
c("evening", "night"),
c("mid-day", "night"))
box_avg_aci_tod<- ggplot(index_timeofday, aes(x=Twilight, y=avg_ACI, fill=Twilight))+
geom_boxplot()+
theme_bw(base_size=15)+
labs(x= " ", y= "ACI - Complexity")+
guides(fill=FALSE)+
stat_compare_means(method="t.test",comparisons = my_comparisons1)+
scale_fill_manual(values=c("lightsteelblue","skyblue3","dodgerblue4","navyblue"))+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
box_avg_bi_tod<- ggplot(index_timeofday, aes(x=Twilight,y= avg_BI, fill=Twilight))+
geom_boxplot()+
theme_bw(base_size=15)+
labs(x= " ", y= "BI - Bioacoustics")+
guides(fill=FALSE)+
stat_compare_means(method="t.test",comparisons = my_comparisons1)+
scale_fill_manual(values=c("lightsteelblue","skyblue3","dodgerblue4","navyblue"))+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
box_avg_ndsi_tod<- ggplot(index_timeofday, aes(x=Twilight,y= avg_NDSI, fill=Twilight))+
geom_boxplot()+
theme_bw(base_size=15)+
labs(x= " ", y= "NDSI - Anthropogenic Disturbance")+
guides(fill=FALSE)+
stat_compare_means(method="t.test",comparisons = my_comparisons1)+
scale_fill_manual(values=c("lightsteelblue","skyblue3","dodgerblue4","navyblue"))+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
plot_grid(box_avg_aci_tod,
box_avg_bi_tod,
box_avg_ndsi_tod,
nrow= 1, align = "v", axis="l",
labels="AUTO")## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_signif()`).## Warning: Removed 2 rows containing non-finite outside the scale range
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## (`stat_signif()`).## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_signif()`).
ggplot(filter(index_timeofday, Twilight!="night"&STATION%in%c("GAR_01","GAR_02","GAR_04")), aes(x=DATE, y=avg_ACI, fill=Twilight))+
geom_bar(stat="identity")+
coord_polar()+
theme_bw()+
scale_x_date(labels=date_format("%b-%d"),date_breaks = "1 day")+
scale_fill_manual(values=c("lightsteelblue","skyblue3","dodgerblue4","navyblue"))+
labs(title="Restoration sites") 
ggplot(filter(index_timeofday, Twilight!="night"&STATION%in%c("RF_01","RF_02")), aes(x=DATE, y=avg_ACI, fill=Twilight))+
geom_bar(stat="identity")+
coord_polar()+
theme_bw()+
scale_x_date(labels=date_format("%b-%d"),date_breaks = "1 day")+
scale_fill_manual(values=c("lightsteelblue","skyblue3","dodgerblue4","navyblue"))+
labs(title="Reference sites")
ggplot(index_timeofday, aes(x=DATE, y=avg_ACI, color=STATION))+
facet_grid(STATION~Twilight)+
geom_point(aes(size=avg_ACI))+
theme_bw()+
theme(legend.position="bottom")+
labs(y="Mean ACI - complexity", x="Julian day", color="")+
scale_x_date(date_labels = "%j")+
scale_size_continuous(range = c(1, 3.5))+
guides(size=FALSE, color=FALSE)## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
a<-ggplot(index_data,aes(x=DATE,y=hour(TIME),fill=ACI))+
facet_wrap(~STATION, ncol=1)+
geom_raster()+
theme_classic()+
theme(legend.position="bottom")+
scale_fill_viridis(option ="turbo",limits = c(92, 1565))+
scale_x_date(date_labels = "%j")+
labs(x="Julian day", y="Hour of the day", fill="ACI", title="Complexity")
b<-ggplot(index_data,aes(x=DATE,y=hour(TIME),fill=BI))+
facet_wrap(~STATION, ncol=1)+
geom_raster()+
theme_classic()+
theme(legend.position="bottom")+
scale_fill_viridis(option ="rocket",limits = c(14,251))+
scale_x_date(date_labels = "%j")+
labs(x="Julian day", y="Hour of the day", fill="BI", title="Bioacoustics")
c<-ggplot(index_data,aes(x=DATE,y=hour(TIME),fill=NDSI))+
facet_wrap(~STATION, ncol=1)+
geom_raster()+
theme_classic()+
theme(legend.position="bottom")+
scale_fill_viridis(option ="mako",limits = c(-1,1))+
scale_x_date(date_labels = "%j")+
labs(x="Julian day", y="Hour of the day", fill="NDSI", title="Anthropogenic Disturbance")
plot_grid(a,
b,
c,
ncol= 3,
labels="AUTO")## Warning: Raster pixels are placed at uneven horizontal intervals and will be shifted
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## (`geom_raster()`).## Warning: Raster pixels are placed at uneven horizontal intervals and will be shifted
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## (`geom_raster()`).
BirdNet Species Summaries
These data are coming from Cornell’s BirdNET Analyzer. It is essentially a GUI version of their popular Merlin app. It requires a lot of decisions on how to analyze your recordings. The user selects confidence levels, sensitiviy, and overlap. The data crunched below is the first deployment (n=10) and is using very lax confidence levels. It will require manual verification (especially for rare species) and the accuracy should be tested. However, we are currently in the stage of seeing how we can best visualize the data. In the near future, we will be testing difference methods to get the most accurate representation of species presence and community composition.
The summary below counts the amount of unique species identified by BirdNET at each site. These data will be verified to eliminate false detections. The software does a good job of identifying common birds but will occasionally include a few ecologically unreasonable detections. These generally account for very few detections (i.e. <0.01% of all detections), so the numbers appear more inflated in the summaries.
n_distinct(species_data_birdnet$Scientific_Name)## [1] 288setDT(species_data_birdnet)[, .(count = uniqueN(Scientific_Name)), by = Station]## Station count
## <char> <int>
## 1: GAR_01 227
## 2: GAR_02 199
## 3: RF_01 251
## 4: RF_02 222setDT(species_data_birdnet)[, .(count = uniqueN(Scientific_Name)), by = Site]## Site count
## <char> <int>
## 1: Garrison 254
## 2: Russell Farm 267guild_count<- setDT(species_data_birdnet)[, .(count = uniqueN(Scientific_Name)), by = Guild]
species_breakdown <- species_data_birdnet %>%
group_by(Station, Scientific_Name) %>%
summarize(Count = n()) %>%
mutate(Percent = round(Count / sum(Count) * 100, 2))## `summarise()` has grouped output by 'Station'. You can override using the
## `.groups` argument.species_over_1p<- species_breakdown %>%
mutate(Percent_1p = case_when(
Percent > 0.01~ "Above",
Percent < 0.01 ~ "Below",
Percent == 0.01 ~ "At")
%>% as.factor())
species_data_birdnet2 <- species_data_birdnet %>%
mutate(Site_Type = case_when(
Station == "RF_01" ~ "Reference",
Station == "RF_02" ~ "Reference",
Station == "GAR_01" ~ "Wild Card",
Station == "GAR_02" ~ "Restoration",
Station == "GAR_03" ~ "Wild Card",
Station == "GAR_04" ~ "Restoration")
%>% as.factor())
RF_01_spps_breakdown <- species_data_birdnet %>%
filter(Station == "RF_01") %>%
group_by(Scientific_Name) %>%
summarize(Count = n()) %>%
mutate(Percent = round(Count / sum(Count) * 100, 2))
RF_02_spps_breakdown <- species_data_birdnet %>%
filter(Station == "RF_02") %>%
group_by(Scientific_Name) %>%
summarize(Count = n()) %>%
mutate(Percent = round(Count / sum(Count) * 100, 2))
GAR_01_spps_breakdown <- species_data_birdnet %>%
filter(Station == "GAR_01") %>%
group_by(Scientific_Name) %>%
summarize(Count = n()) %>%
mutate(Percent = round(Count / sum(Count) * 100, 2))
GAR_02_spps_breakdown <- species_data_birdnet %>%
filter(Station == "GAR_02") %>%
group_by(Scientific_Name) %>%
summarize(Count = n()) %>%
mutate(Percent = round(Count / sum(Count) * 100, 2))BirdNet Species Outputs
These data are coming from Cornell’s BirdNET Analyzer. It is essentially a GUI version of their popular Merlin app. It requires a lot of decisions on how to analyze your recordings. The user selects confidence levels, sensitiviy, and overlap. The data crunched below is the first deployment (n=10) and is using very lax confidence levels. It will require manual verification (especially for rare species) and the accuracy should be tested. However, we are currently in the stage of seeing how we can best visualize the data. In the near future, we will be testing difference methods to get the most accurate representation of species presence and community composition.
Data is visualized in two ways, common species and guilds. Guilds gives a good overview of how sites vary (i.e. more shorebirds and ducks in a mudflat). Below the graphics will be referencing the same dataset, but either calling out specific common bird species or guild.
Species Diversity
In future, want to annotate the deployment times this represents. Maybe just in title. Currently the species graphics are only for the first deployments of 2023. Running BirdNET requires a lot of time and processing power, so we have only completed one round of processing.Will need to update this if it is ever worth the effort
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Guild breakdown for all sites during the first deployment (4/26/23 to 5/7/23).
## Warning: The dot-dot notation (`..prop..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(prop)` instead.
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## generated.
Breakdown of common bird species by site during the first deployment (4/26/23 to 5/7/23). Four-letter alpha codes represent common name of bird species. Seaside sparrow (SESP) is by far the most dominant species with 380,311 total detections.
Guild breakdown by site during the first deployment (4/26/23 to 5/7/23). Passerines were the most common guild represented in the data due to the high level of SESP detections.
Cowplot for Species Diversity
Grouping the above graphics by their site types (restoration, reference, wildcard) just to play around with grouping. Will probably group by habitat type in the future. I think that gives more context to what is going on with the species diversity. However, the site type graphics will be important to have during the post-restoration data collection.
# Common Birds
Restore_common <- ggplot(species_data_birdnet2 %>% filter(Site_Type == "Restoration"), aes(x= Common_Birds, group= Station)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +
geom_text(aes( label = scales::percent(..prop..,accuracy=0.01),
y= ..prop.. ), stat= "count", vjust =- 1.5, size= 3) +
labs(x= " ", y = "Percent", fill="Common_Birds") +
guides(fill= FALSE)+
theme_bw(base_size=15)+
facet_grid(~Station) +
labs(subtitle = "Restoration Sites") +
scale_y_continuous(labels = scales::percent)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Refer_common <- ggplot(species_data_birdnet2 %>% filter(Site_Type == "Reference"), aes(x= Common_Birds, group= Station)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +
geom_text(aes( label = scales::percent(..prop..,accuracy=0.1),
y= ..prop.. ), stat= "count", vjust =- 1.5, size= 2.5) +
labs(x= " ", y = "Percent", fill="Lower_Guild") +
guides(fill= FALSE)+
theme_bw(base_size=18)+
facet_grid(~Station) +
labs(subtitle = "Reference Sites") +
scale_y_continuous(labels = scales::percent)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Wild_common <- ggplot(species_data_birdnet2 %>% filter(Site_Type == "Wild Card"), aes(x= Common_Birds, group= Station)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +
geom_text(aes( label = scales::percent(..prop..,accuracy=0.1),
y= ..prop.. ), stat= "count", vjust =- 1.5, size= 2.5) +
labs(x= "Common Bird Species Alpha Code", y = "Percent", fill="Lower_Guild") +
guides(fill= FALSE)+
theme_bw(base_size=18)+
facet_grid(~Station) +
labs(subtitle = "Wild Card Sites") +
scale_y_continuous(labels = scales::percent)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
# By Guild
Restore_guild <- ggplot(species_data_birdnet2 %>% filter(Site_Type == "Restoration"), aes(x= Lower_Guild, group= Station)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +
geom_text(aes( label = scales::percent(..prop..,accuracy=0.01),
y= ..prop.. ), stat= "count", vjust =- 1.5, size= 3) +
labs(x= " ", y = "Percent", fill="Common_Birds") +
guides(fill= FALSE)+
theme_bw(base_size=15)+
facet_grid(~Station) +
labs(subtitle = "Restoration Sites") +
scale_y_continuous(labels = scales::percent)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Refer_guild <- ggplot(species_data_birdnet2 %>% filter(Site_Type == "Reference"), aes(x= Lower_Guild, group= Station)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +
geom_text(aes( label = scales::percent(..prop..,accuracy=0.1),
y= ..prop.. ), stat= "count", vjust =- 1.5, size= 2.5) +
labs(x= " ", y = "Percent", fill="Lower_Guild") +
guides(fill= FALSE)+
theme_bw(base_size=18)+
facet_grid(~Station) +
labs(subtitle = "Reference Sites") +
scale_y_continuous(labels = scales::percent)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Wild_guild <- ggplot(species_data_birdnet2 %>% filter(Site_Type == "Wild Card"), aes(x= Lower_Guild, group= Station)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +
geom_text(aes( label = scales::percent(..prop..,accuracy=0.1),
y= ..prop.. ), stat= "count", vjust =- 1.5, size= 2.5) +
labs(x= "Guild", y = "Percent", fill="Lower_Guild") +
guides(fill= FALSE)+
theme_bw(base_size=18)+
facet_grid(~Station) +
labs(subtitle = "Wild Card Sites") +
scale_y_continuous(labels = scales::percent)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
title1 = "Common Bird Species By Site Type"
title2 = "Bird Guild By Site Type"
plot_grid(Restore_common,
Refer_common,
Wild_common,
labels = title1, label_size = 14, hjust = -2,
nrow= 3, align = "v", axis="l")
Common bird species breakdown by site type (Restoration, Reference, Wild Card) during the first deployment of 2023 (4/26/23 to 5/7/23).
plot_grid(Restore_guild,
Refer_guild,
Wild_guild,
labels = title2, label_size = 14, hjust = -3,
nrow= 3, align = "v", axis="l")
Common bird species breakdown by site type (Restoration, Reference, Wild Card) during the first deployment of 2023 (4/26/23 to 5/7/23).
Summary Tables
Gives further context to the data, but in a tabular form rather than graphically. Broken down by site, station, acoustic indices, site types, and condfidence levels.
st(index_data %>% dplyr::select(c("SITE", "STATION", "NDSI", "ACI", "ADI" , "AEI", "BI")))| Variable | N | Mean | Std. Dev. | Min | Pctl. 25 | Pctl. 75 | Max |
|---|---|---|---|---|---|---|---|
| SITE | 100742 | ||||||
| … Garrison | 55994 | 56% | |||||
| … Russell Farm | 44748 | 44% | |||||
| STATION | 100742 | ||||||
| … GAR_01 | 5636 | 6% | |||||
| … GAR_02 | 23782 | 24% | |||||
| … GAR_03 | 5712 | 6% | |||||
| … GAR_04 | 20864 | 21% | |||||
| … RF_01 | 20228 | 20% | |||||
| … RF_02 | 24520 | 24% | |||||
| NDSI | 98700 | 0.64 | 0.39 | -0.95 | 0.38 | 0.95 | 1 |
| ACI | 98700 | 720 | 101 | 77 | 657 | 742 | 1565 |
| ADI | 98700 | 1.5 | 0.58 | 0 | 1.1 | 1.9 | 2.3 |
| AEI | 98700 | 0.58 | 0.21 | 0 | 0.44 | 0.75 | 0.9 |
| BI | 98700 | 106 | 42 | 16 | 69 | 139 | 250 |
st(avg_index2 %>% dplyr::select(c("SITE", "STATION", "Site_Type", "avg_NDSI", "avg_ACI", "avg_ADI" , "avg_AEI", "avg_BI")))#,## Adding missing grouping variables: `DATETIME`, `DATE`| Variable | N | Mean | Std. Dev. | Min | Pctl. 25 | Pctl. 75 | Max |
|---|---|---|---|---|---|---|---|
| SITE | 49352 | ||||||
| … Garrison | 26978 | 55% | |||||
| … Russell Farm | 22374 | 45% | |||||
| STATION | 49352 | ||||||
| … GAR_01 | 2818 | 6% | |||||
| … GAR_02 | 11891 | 24% | |||||
| … GAR_03 | 2819 | 6% | |||||
| … GAR_04 | 9450 | 19% | |||||
| … RF_01 | 10114 | 20% | |||||
| … RF_02 | 12260 | 25% | |||||
| Site_Type | 49352 | ||||||
| … Wild Card | 5637 | 11% | |||||
| … Restoration | 21341 | 43% | |||||
| … Reference | 22374 | 45% | |||||
| avg_NDSI | 49350 | 0.64 | 0.37 | -0.94 | 0.38 | 0.94 | 1 |
| avg_ACI | 49350 | 720 | 92 | 79 | 660 | 747 | 1332 |
| avg_ADI | 49350 | 1.5 | 0.54 | 0.001 | 1.1 | 1.9 | 2.3 |
| avg_AEI | 49350 | 0.58 | 0.2 | 0 | 0.45 | 0.73 | 0.9 |
| avg_BI | 49350 | 106 | 41 | 20 | 71 | 138 | 235 |
#"sd_NDSI","sd_ACI", "sd_ADI" , "sd_AEI", "sd_BI")))
st(species_data_birdnet2 %>% dplyr::select(c("Site", "Station", "Confidence", "Site_Type")))| Variable | N | Mean | Std. Dev. | Min | Pctl. 25 | Pctl. 75 | Max |
|---|---|---|---|---|---|---|---|
| Site | 886670 | ||||||
| … Garrison | 454358 | 51% | |||||
| … Russell Farm | 432312 | 49% | |||||
| Station | 886670 | ||||||
| … GAR_01 | 220267 | 25% | |||||
| … GAR_02 | 234091 | 26% | |||||
| … RF_01 | 175198 | 20% | |||||
| … RF_02 | 257114 | 29% | |||||
| Confidence | 883874 | 0.65 | 0.29 | 0.17 | 0.37 | 0.94 | 1 |
| Site_Type | 886670 | ||||||
| … Reference | 432312 | 49% | |||||
| … Restoration | 234091 | 26% | |||||
| … Wild Card | 220267 | 25% |
st(species_data_birdnet %>% dplyr::select(c("Site", "Station", "Confidence", "Common_Birds")), group= 'Common_Birds', group.long = TRUE)| Variable | N | Mean | Std. Dev. | Min | Pctl. 25 | Pctl. 75 | Max |
|---|---|---|---|---|---|---|---|
| Common_Birds: CAGO | |||||||
| Site | 4898 | ||||||
| … Garrison | 3862 | 79% | |||||
| … Russell Farm | 1036 | 21% | |||||
| Station | 4898 | ||||||
| … GAR_01 | 2019 | 41% | |||||
| … GAR_02 | 1843 | 38% | |||||
| … RF_01 | 624 | 13% | |||||
| … RF_02 | 412 | 8% | |||||
| Confidence | 4898 | 0.46 | 0.24 | 0.17 | 0.25 | 0.63 | 1 |
| Common_Birds: CLRA | |||||||
| Site | 131722 | ||||||
| … Garrison | 72327 | 55% | |||||
| … Russell Farm | 59395 | 45% | |||||
| Station | 131722 | ||||||
| … GAR_01 | 16318 | 12% | |||||
| … GAR_02 | 56009 | 43% | |||||
| … RF_01 | 16779 | 13% | |||||
| … RF_02 | 42616 | 32% | |||||
| Confidence | 131722 | 0.59 | 0.26 | 0.17 | 0.35 | 0.84 | 1 |
| Common_Birds: CWWI | |||||||
| Site | 12576 | ||||||
| … Garrison | 825 | 7% | |||||
| … Russell Farm | 11751 | 93% | |||||
| Station | 12576 | ||||||
| … GAR_01 | 823 | 7% | |||||
| … GAR_02 | 2 | 0% | |||||
| … RF_01 | 4182 | 33% | |||||
| … RF_02 | 7569 | 60% | |||||
| Confidence | 12425 | 0.83 | 0.23 | 0.17 | 0.74 | 0.99 | 1 |
| Common_Birds: EWPW | |||||||
| Site | 11127 | ||||||
| … Garrison | 11113 | 100% | |||||
| … Russell Farm | 14 | 0% | |||||
| Station | 11127 | ||||||
| … GAR_01 | 10851 | 98% | |||||
| … GAR_02 | 262 | 2% | |||||
| … RF_01 | 10 | 0% | |||||
| … RF_02 | 4 | 0% | |||||
| Confidence | 11127 | 0.82 | 0.24 | 0.17 | 0.73 | 0.99 | 1 |
| Common_Birds: GRYE | |||||||
| Site | 19381 | ||||||
| … Garrison | 9941 | 51% | |||||
| … Russell Farm | 9440 | 49% | |||||
| Station | 19381 | ||||||
| … GAR_01 | 1852 | 10% | |||||
| … GAR_02 | 8089 | 42% | |||||
| … RF_01 | 3080 | 16% | |||||
| … RF_02 | 6360 | 33% | |||||
| Confidence | 19381 | 0.58 | 0.28 | 0.17 | 0.32 | 0.86 | 1 |
| Common_Birds: GWTE | |||||||
| Site | 25044 | ||||||
| … Garrison | 15625 | 62% | |||||
| … Russell Farm | 9419 | 38% | |||||
| Station | 25044 | ||||||
| … GAR_01 | 852 | 3% | |||||
| … GAR_02 | 14773 | 59% | |||||
| … RF_01 | 7544 | 30% | |||||
| … RF_02 | 1875 | 7% | |||||
| Confidence | 25044 | 0.67 | 0.29 | 0.17 | 0.4 | 0.95 | 1 |
| Common_Birds: KIRA | |||||||
| Site | 14953 | ||||||
| … Garrison | 5023 | 34% | |||||
| … Russell Farm | 9930 | 66% | |||||
| Station | 14953 | ||||||
| … GAR_01 | 1927 | 13% | |||||
| … GAR_02 | 3096 | 21% | |||||
| … RF_01 | 6217 | 42% | |||||
| … RF_02 | 3713 | 25% | |||||
| Confidence | 14953 | 0.41 | 0.23 | 0.17 | 0.23 | 0.55 | 1 |
| Common_Birds: LAGU | |||||||
| Site | 24834 | ||||||
| … Garrison | 13453 | 54% | |||||
| … Russell Farm | 11381 | 46% | |||||
| Station | 24834 | ||||||
| … GAR_01 | 4221 | 17% | |||||
| … GAR_02 | 9232 | 37% | |||||
| … RF_01 | 6795 | 27% | |||||
| … RF_02 | 4586 | 18% | |||||
| Confidence | 24834 | 0.58 | 0.27 | 0.17 | 0.32 | 0.84 | 1 |
| Common_Birds: LEYE | |||||||
| Site | 9951 | ||||||
| … Garrison | 4161 | 42% | |||||
| … Russell Farm | 5790 | 58% | |||||
| Station | 9951 | ||||||
| … GAR_01 | 503 | 5% | |||||
| … GAR_02 | 3658 | 37% | |||||
| … RF_01 | 2297 | 23% | |||||
| … RF_02 | 3493 | 35% | |||||
| Confidence | 9951 | 0.51 | 0.26 | 0.17 | 0.27 | 0.74 | 1 |
| Common_Birds: MAWR | |||||||
| Site | 68782 | ||||||
| … Garrison | 66257 | 96% | |||||
| … Russell Farm | 2525 | 4% | |||||
| Station | 68782 | ||||||
| … GAR_01 | 10603 | 15% | |||||
| … GAR_02 | 55654 | 81% | |||||
| … RF_01 | 2505 | 4% | |||||
| … RF_02 | 20 | 0% | |||||
| Confidence | 68782 | 0.66 | 0.27 | 0.17 | 0.41 | 0.92 | 1 |
| Common_Birds: OSPR | |||||||
| Site | 41246 | ||||||
| … Garrison | 1185 | 3% | |||||
| … Russell Farm | 40061 | 97% | |||||
| Station | 41246 | ||||||
| … GAR_01 | 857 | 2% | |||||
| … GAR_02 | 328 | 1% | |||||
| … RF_01 | 23109 | 56% | |||||
| … RF_02 | 16952 | 41% | |||||
| Confidence | 41246 | 0.72 | 0.28 | 0.17 | 0.48 | 0.98 | 1 |
| Common_Birds: Other | |||||||
| Site | 97581 | ||||||
| … Garrison | 47441 | 49% | |||||
| … Russell Farm | 50140 | 51% | |||||
| Station | 97581 | ||||||
| … GAR_01 | 19279 | 20% | |||||
| … GAR_02 | 28162 | 29% | |||||
| … RF_01 | 30254 | 31% | |||||
| … RF_02 | 19886 | 20% | |||||
| Confidence | 95266 | 0.49 | 0.28 | 0.17 | 0.25 | 0.73 | 1 |
| Common_Birds: RWBL | |||||||
| Site | 8748 | ||||||
| … Garrison | 1172 | 13% | |||||
| … Russell Farm | 7576 | 87% | |||||
| Station | 8748 | ||||||
| … GAR_01 | 945 | 11% | |||||
| … GAR_02 | 227 | 3% | |||||
| … RF_01 | 6298 | 72% | |||||
| … RF_02 | 1278 | 15% | |||||
| Confidence | 8748 | 0.36 | 0.18 | 0.17 | 0.22 | 0.45 | 0.99 |
| Common_Birds: SESP | |||||||
| Site | 380311 | ||||||
| … Garrison | 189103 | 50% | |||||
| … Russell Farm | 191208 | 50% | |||||
| Station | 380311 | ||||||
| … GAR_01 | 139249 | 37% | |||||
| … GAR_02 | 49854 | 13% | |||||
| … RF_01 | 51097 | 13% | |||||
| … RF_02 | 140111 | 37% | |||||
| Confidence | 380311 | 0.73 | 0.28 | 0.17 | 0.49 | 0.98 | 1 |
| Common_Birds: TRSW | |||||||
| Site | 13255 | ||||||
| … Garrison | 7466 | 56% | |||||
| … Russell Farm | 5789 | 44% | |||||
| Station | 13255 | ||||||
| … GAR_01 | 7455 | 56% | |||||
| … GAR_02 | 11 | 0% | |||||
| … RF_01 | 5485 | 41% | |||||
| … RF_02 | 304 | 2% | |||||
| Confidence | 13255 | 0.65 | 0.28 | 0.17 | 0.39 | 0.93 | 1 |
| Common_Birds: WEVI | |||||||
| Site | 5597 | ||||||
| … Garrison | 10 | 0% | |||||
| … Russell Farm | 5587 | 100% | |||||
| Station | 5597 | ||||||
| … GAR_01 | 10 | 0% | |||||
| … GAR_02 | 0 | 0% | |||||
| … RF_01 | 5354 | 96% | |||||
| … RF_02 | 233 | 4% | |||||
| Confidence | 5597 | 0.52 | 0.25 | 0.17 | 0.29 | 0.73 | 1 |
| Common_Birds: WILL | |||||||
| Site | 16264 | ||||||
| … Garrison | 5198 | 32% | |||||
| … Russell Farm | 11066 | 68% | |||||
| Station | 16264 | ||||||
| … GAR_01 | 2443 | 15% | |||||
| … GAR_02 | 2755 | 17% | |||||
| … RF_01 | 3488 | 21% | |||||
| … RF_02 | 7578 | 47% | |||||
| Confidence | 16264 | 0.63 | 0.28 | 0.17 | 0.36 | 0.92 | 1 |
st(species_breakdown)| Variable | N | Mean | Std. Dev. | Min | Pctl. 25 | Pctl. 75 | Max |
|---|---|---|---|---|---|---|---|
| Station | 899 | ||||||
| … GAR_01 | 227 | 25% | |||||
| … GAR_02 | 199 | 22% | |||||
| … RF_01 | 251 | 28% | |||||
| … RF_02 | 222 | 25% | |||||
| Count | 899 | 986 | 7760 | 1 | 4 | 76 | 140111 |
| Percent | 899 | 0.44 | 3.4 | 0 | 0 | 0.04 | 63 |